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The increasing complexity of Cyber-Physical Systems (CPS) increases the difficulty for users to understand their behavior. Using existing Explainable Artificial Intelligence (XAI) methods, CPS can explain their behavior to the users. However, the input-output correlations used in XAI methods are not capable of explaining certain anomalies in CPS behavior caused by contextual influences (CIs) since they do not consider the context of the CPS. Some well-known techniques used for understanding such CIs on CPS are test chambers and the analysis of logged CPS data. However, test chambers are typically only available to the manufacturer of a CPS, thus not useful for understanding CIs on the shop floors. Data analysis methods focus on data correlations, which are insufficient to explain causal relationships without using expert (human) knowledge. Hence, we propose a context-aware log-based explanation system to explain the causal relationship between CIs and the behavior of a CPS. The proposed solution employs semantic technologies to access the context of the CPS. It demonstrates the causal relationship between the CPS and CIs through counterfactual explanation and abductive reasoning methods. The contextual explanations offered by the proposed system will assist users in visualizing diverse scenarios in order to improve the CPS’ behavior accordingly.

Research in proactive agents capable of anticipating users’ needs has been conducted in different application areas, from agents aiming to help users accomplish their next task while using a text editor, to digital assistants that provide support to medical professionals in hospitals. Considering these works and following the rapid development in machine learning, the growing adoption of the IoT, the prevalence of pervasive computing, and the various innovative user interfaces that are becoming mainstream, we propose the creation of expert digital companions. Expert digital companions are agents that know the specifics of an environment, the available physical equipment in a space, as well as software components that might be reachable in such an environment. These expert digital companions can interact with personal digital companions, which know the preferences of a specific user. Thus, expert and personal digital companions collaborate to provide customised assistance to a user given her environment. In this paper, we present an implementation of an expert digital companion for employees working in an office and a shop-floor environment that takes advantages of: the decoupling that the W3C WoT Thing Description provides to interact with and control devices and other tools; a Knowledge Graph to provide richer descriptions of elements in the environment; a Computer Vision Algorithm to perceive the physical world; Mixed Reality as the medium to deliver assistance to workers.